Association of asthma genetic variants with asthma‐associated traits reveals molecular pathways of eosinophilic asthma

Abstract Introduction Asthma is a complex, polygenic, heterogenous inflammatory disease. Recently, we generated a list of 128 independent single nucleotide polymorphisms (SNPs) associated with asthma in genome‐wide association studies. However, it is unknown if asthma SNPs are associated with specific asthma‐associated traits such as high eosinophil counts, atopy, and airway obstruction, revealing molecular endotypes of this disease. Here, we aim to identify the association between asthma SNPs and asthma‐associated traits and assess expression quantitative trait locus (e‐QTLs) to reveal their downstream functional effects and find drug targets. Methods Association analyses between 128 asthma SNPs and associated traits (blood eosinophil numbers, atopy, airway obstruction, airway hyperresponsiveness) were conducted using regression modelling in population‐based studies (Lifelines N = 32,817/Vlagtwedde‐Vlaardingen N = 1554) and an asthma cohort (Dutch Asthma genome‐wide association study N = 917). Functional enrichment and pathway analysis were performed with genes linked to the significant SNPs by e‐QTL analysis. Genes were investigated to generate novel drug targets. Results We identified 69 asthma SNPs that were associated with at least one trait, with 20 SNPs being associated with multiple traits. The SNP annotated to SMAD3 was the most pleiotropic. In total, 42 SNPs were associated with eosinophil counts, 18 SNPs with airway obstruction, and 21 SNPs with atopy. We identified genetically driven pathways regulating eosinophilia. The largest network of eosinophilia contained two genes (IL4R, TSLP) targeted by drugs currently available for eosinophilic asthma. Several novel targets were identified such as IL‐18, CCR4, and calcineurin. Conclusion Many asthma SNPs are associated with blood eosinophil counts and genetically driven molecular pathways of asthma‐associated traits were identified.


| INTRODUCTION
Asthma is a complex, heterogeneous inflammatory disease caused by a combination of genetic and environmental factors. It is characterised by respiratory symptoms, such as wheezing and chest tightness, and pathophysiological mechanisms, like reversible airway obstruction, and airway hyper-responsiveness. Previously, asthma was regarded as a single disease entity. However, currently, distinct phenotypes of asthma are recognised due to an increased understanding of its underlying mechanisms. These asthma phenotypes are based on clinical presentation, inflammatory patterns, severity and triggers and/or pathophysiological mechanisms. Thus, studies into the pathogenic mechanisms of asthma should address this disease heterogeneity. 1 Genome-wide association study (GWAS) is an approach that can be used to reveal the underlying mechanisms by identifying single nucleotide polymorphisms (SNPs) that are associated with asthma.
Recently, we described a list of 128 independent single nucleotide polymorphisms that were previously associated with asthma in European/white populations at a genome-wide significant level. 2 These SNPs are associated with asthma in general; however, it is unknown if these SNPs are involved in specific traits such as eosinophil counts, atopy or airway obstruction. Associations of asthma SNPs with a specific asthma-associated trait may reveal a specific endotype, driven by a distinct molecular network.
It has been previously observed that many asthma SNPs associate with different asthma related phenotypes, indicating potential pleiotropic effects. 3,4 Analysing the association between asthma SNPs and asthma-associated traits in the general population may therefore reveal insights into the genetic architecture of this disease.
Alternatively, asthma SNPs may only act in a disease-specific manner, which would result in associations with traits only in asthma populations, but not in the general population.
In this paper, we analysed the association of asthma SNPs with various associated traits in the general population as well as in asthma patients only; next, we performed expression quantitative trait loci (e-QTL) and pathway analyses to reveal the downstream functional effects of SNPs associated with specific traits and test the hypothesis that these form distinct molecular pathways. Finally, we verify if the implicated genes were already targeted by known asthma drugs and propose new drug targets and drugs for asthma, by repositioning drugs for other indications based on genetic evidence.

| Study design
SNP selection was based on our previous review, where we performed a systematic analysis of GWAS to identify SNPs associated with asthma at genome-wide significant level. 2 This resulted in a list of 128 independent asthma-associated SNPs, which we call asthma SNPs in the remainder of this paper.
For this study, we conducted the analyses in three independent Dutch general population and patient cohorts as shown in Table 1.
The first is the Lifelines cohort (n = 32,817) which represents a general population cohort, comprising two and three-generation families. 5 In this cohort, we tested the association between asthma SNPs and eosinophil counts and airway obstruction (FEV 1 , FEV 1 / FVC) in both the general population and asthma patients. In addition, we tested the association between asthma SNPs and childhood-onset asthma. 6 The second cohort is the Vlagtwedde-Vlaardingen (Vlw-Vld) T A B L E 1 Description of the Study population. Abbreviations: N, number of subjects data field available for; n.a, not available; SD, standard of deviation.

Characteristics
a Childhood onset asthma defined as a diagnosis occurring before the age of 18. general population-based cohort n = 1554. 7 Here, we investigated the association of asthma SNPs with eosinophil counts, airway  9 and GTEx. 10 For each trait, the associated SNPs were annotated to eGenes by eQTL-analysis, and we then performed functional enrichment, pathway and protein-interaction analyses.

| Statistical methods
Linear or logistic regression analyses of SNPs with asthma-associated traits were performed in SAIGEgds, 11 which accounts for the correlation between family members in the Lifelines cohort and Plink v1.90p 12 (Vlw-Vld and DAG) using an additive model. For the general population cohorts, age, sex and asthma were included as covariates.
For the asthma cohorts, age (excluding the analysis of age of onset) and sex were included as covariates. Meta-analyses were performed in Plink using a fixed model. We predefined two significance thresholds: adjusted Pvalue<0.05 (Bonferroni correction over 128 SNPs), and nominal, non-adjusted p value < 0.05. The expected direction of the association was taken into consideration: positive association between the asthma-associated risk allele and eosinophil counts, IgE, skin test, AHR, and childhood-onset asthma and negative association between the asthma-associated risk allele and FEV 1 , FEV 1 /FVC.
The functional consequences of the risk SNPs on gene expression were investigated using published cis-eQTL datasets (eQTLGen, GTEx) from whole blood for SNPs associated with eosinophils, IgE, and childhood-onset asthma, whole blood and lung for SNPs associated with airway obstruction and AHR, and whole blood and skin for SNPs associated with skin test. The selection of eGenes was based on the significance of p-value below 2.3*10 −9 , as reported previously. 2 Functional enrichment analysis was performed on eGenes associated with blood eosinophil counts, by mapping genes to g:GOST on g:Profiler (biit.cs.ut.ee/gprofiler) to detect statistically significant enriched terms. The data sources that were used in g:profiler are Gene Ontology (molecular function, biological process), KEGG,

| Study populations
For the general population analyses (N = 25,371) we used Lifelines and VLW-VLD cohorts and for the asthma population analyses (N = 3840) we used Lifelines (only asthma) and DAG cohorts.
The description of the study populations is shown in Table 1.

| Genetic association with asthma-associated traits
Out of the 128 asthma SNPs, a total of 123, 120, and 122 SNPs in LifeLines, Vlw-Vld and DAG cohorts, respectively, were available for analysis.
In the general population, 38 asthma SNPs were associated with eosinophil counts, 16 of which had a p adj < 0.05. In the asthma popu- representation of these results is provided in Figure 1, whereas full results can be found in the Online Supplementary Tables S1 and S2.

| Pleiotropy
Whereas all asthma SNPs were selected based on prior evidence of their association with asthma, we also inspected further overlap in association with associated traits (Figure 2, Supplementary Table S3).
We observed that there is 1 SNP (rs56375023, located in SMAD3) that was related to three associated traits: blood eosinophil counts, airway obstruction and atopy. We observed that 9 asthma SNPs were associated with eosinophil counts and atopy (located in or near F I G U R E 1 Association of asthma single nucleotide polymorphisms (SNPs) with asthma-associated traits. The blue and red colours display the direction of the BETA value. Asthma-associated allele is taken as the risk allele: blue shows higher levels (positive association) and red shows lower levels (negative association). The colour intensity represents the significance of the association between the SNP and the phenotype (Dark red or dark blue: p adj < 0.05; Light red or light blue: p < 0.05).

F I G U R E 2
Overlap of single nucleotide polymorphisms (SNPs) associated with asthma-associated traits. AHR, Airway hyper-responsiveness; COA, Childhood-onset asthma.

| Disease specificity
We observed several asthma SNPs to be associated with one or more asthma-associated traits in asthma patients, but not in the general population ( Figure 3).

| Functional genetics
After conducting the cis-eQTL analysis, gene expression levels of 120 unique genes were significantly associated with SNPs that showed association with asthma traits. For SNPs associated with blood eosinophil counts, we identified 82 eGenes. Amongst these genes, we identified genes encoding drug targets for eosinophilic asthma, such as IL4RA and TSLP. Some potential targets and their role in eosinophilia are mentioned in Supplementary Table S4

| Network and pathway analysis
In the functional enrichment analysis, 114 term names were generated for blood eosinophil counts via g:Profiler (Supplementary Table   S6). The most relatively specific repeated terms in the enrichment analysis were: T cells (differentiation, activation, and apoptotic process), T-helper Type 1 response, Interleukin-4, Interleukin-18 pathways, and Calcineurin signalling as shown in Figure 4.

| DISCUSSION
Asthma is a complex, polygenic, heterogeneous inflammatory disease.
One hypothesis is that this polygenic origin may relate to the broad disease definition used in genetic studies; with the outcome 'asthma' capturing multiple disease endotypes. Our analysis shows that many asthma SNPs have associations with asthma-associated traits. In our F I G U R E 3 Asthma-specific single nucleotide polymorphisms (SNPs). The blue and red colours display the direction of the BETA value. Asthma-associated allele is taken as the risk allele: blue shows higher levels (positive association) and red shows lower levels (negative association). The colour intensity represents the significance of the association between the SNP and the phenotype (Dark red or dark blue: p adj < 0.05; Light red or light blue: p < 0.05).
EL-HUSSEINI ET AL. study, we show a clear association of asthma SNPs with eosinophil counts and, to a lesser degree, with airway obstruction. We report fewer associations of asthma SNPs with total IgE, skin test and AHR.
Unfortunately, fractional exhaled nitric oxide (FeNO), which is another interesting type 2 inflammatory biomarker, was not present in the cohorts under study. We observed pleiotropic effects of 20 asthma SNPs but also observed that 22 SNPs had an association with an associated trait in asthma patients only. Our results, therefore, provide evidence for pleiotropic, as well as trait-specific effects.
Based on the shared association of asthma SNPs with blood eosinophil counts, we identified genetically driven pathways that may regulate blood eosinophil counts in asthma, which could be targets of future interventions for eosinophilic asthma.
Blood eosinophil counts were associated with 38 asthma SNPs in the general population. Our findings are consistent with previous reports in studies on subjects of European ancestry, in which nearly 84% of the asthma SNPs showed association with eosinophil counts 13,14 (Supplemental Table S7). Ten asthma SNPs that were associated with blood eosinophil counts in the general population were also associated with eosinophil counts in asthma. These asthma-associated SNPs may constitute genetic susceptibility to eosinophilic asthma; as blood eosinophilia correlates with airway eosinophilia in asthma patients. 15 However, we recognise that we might have underestimated the number of these SNPs, as we take statistical significance as the main criterium of association and were relatively underpowered in the asthma cohorts compared to the general population. Therefore, we present the nominal p-value together with direction of effect in the Supplementary Tables S1 and S2 to better assess the consistency of the results.
Although all 128 SNPs were previously reported to be associated with asthma at genome wide significance in other populations, we observed that some SNPs were not associated with asthma related traits in our included cohorts. This may be due to lack of power, or that specific related phenotypes for that SNP (i.e., neutrophilic asthma) were not investigated in our study. Alternatively, association with asthma related traits may be partly due to  16 Another asthma-specific target is IL-7R, which acts as a receptor for interleukin-7 (IL-7) and TSLP. IL-7 and TSLP may contribute to airway inflammation by promoting activation and survival of eosinophils. 17 We also observed potential targets such as PPOX, TOMM40L and NDUSF1 that are involved in mitochondrial dysfunction and oxidative stress which when activated in the inflammatory cells lead to tissue injury in the bronchial epithelium. 18 One asthma SNP had strongly pleiotropic effects. This SNP rs56375023 [SMAD3] was associated with blood eosinophil counts in both the general and asthma populations, low FEV 1 /FVC in the general population and IgE sensitisation in the asthma population.
SMAD3 is an intracellular transducer that mediates TGF-ß signalling.
TGF-ß is a multifunctional cytokine. It has anti-inflammatory roles inhibiting Th1 and Th2 differentiation and maturation of macrophages and dendritic cells, and it also induces T regulatory cells. [19][20][21][22] On the other hand, TGF-ß has a proinflammatory effect by enhancing leucocyte recruitment, IgA production, chemoattracting leucocytes and modulating adhesion molecules. 23,24 A study on SMAD3 knockout mice revealed increases in Th2 and IL17 proinflammatory pathways and also induced regulatory elements like Foxp3. 25 TGF-ß also plays a role in airway remodelling and structural cell proliferation via Smad3 which leads to airway obstruction and AHR. 26 Our results on pleiotropic genes may explain the correlation between these different traits in asthma, either by genes playing a pleiotropic role in the development of these phenotypes or through causal pathways, which would imply that one trait causes the occurrence of another trait.
We generated asthma trait-associated lists of SNPs that were annotated to potential drug targets. It has been previously shown that the success of drug development doubles when drug targets have genetic evidence, compared to those without genetic evidence. 27 Two eGenes related to SNPs for eosinophilic asthma encode for drug targets for eosinophilic, type 2 asthma: IL4R (dupilimab) and TSLP (tezepelumab). 28 These findings support the validity of our genomics-guided drug discovery approach and suggest that some of the other genes identified may represent novel drug targets for eosinophilic asthma ( calcineurin inhibitors in asthma is limited because of their systemic adverse effects, which may be limited if these drugs could be applied locally.
Currently, there is an unmet need for drugs positioned for noneosinophilic asthma. In this study, we have identified SNPs associated with low eosinophil counts. One of the interesting genes is TNFSF4 (OX40L) which plays a role in modulating airway inflammation in asthma. Oxelumab is an anti-OX40L, which was tested in a small clinical trial for adults with mild allergic asthma without clear beneficial effects. 34 However, our results suggest to test this drug in noneosinophilic, type 2 low asthma.
In conclusion, many asthma-associated GWAS SNPs have distinct associations with asthma traits, such as eosinophilia, airway obstruction and atopy. We found evidence for pleiotropic genes, but also some disease-specific genes were identified. This study shows that there are distinct sets of asthma SNPs that constitute distinct molecular pathways, according to protein interaction networks analysis. We identified genetically driven pathways regulating eosinophilia in asthma, with two genes (IL4R, TSLP) targeted by drugs currently available for eosinophilic asthma.